Interpretable Nonlinear Dynamic Modeling of Neural Trajectories [article]

Yuan Zhao, Il Memming Park
2016 arXiv   pre-print
A central challenge in neuroscience is understanding how neural system implements computation through its dynamics. We propose a nonlinear time series model aimed at characterizing interpretable dynamics from neural trajectories. Our model assumes low-dimensional continuous dynamics in a finite volume. It incorporates a prior assumption about globally contractional dynamics to avoid overly enthusiastic extrapolation outside of the support of observed trajectories. We show that our model can
more » ... ver qualitative features of the phase portrait such as attractors, slow points, and bifurcations, while also producing reliable long-term future predictions in a variety of dynamical models and in real neural data.
arXiv:1608.06546v2 fatcat:vtiwcuvw25ftbcpgtnlvyeqkhe